Work

Revolutionizing endocarditis diagnosis: AI meets metagenomics for rare Bartonella detection

Year: 2025

Type: article

Abstract: Bartonella endocarditis is notoriously challenging to diagnose due to its insidious onset, nonspecific symptoms, and the limitations of conventional culture-based techniques. Artificial intelligence (AI)-driven metagenomic next-generation sequencing (mNGS) represents a transformative diagnostic approach by integrating machine learning algorithms with culture-independent sequencing data to improve accuracy and sensitivity. This technique overcomes barriers such as culture bottlenecks and post-surgical sample limitations, achieving high diagnostic specificity while enabling the detection of rare or novel pathogens. Despite challenges including limited reference databases, contamination risks, and cost-related barriers in low-resource settings, AI-enhanced metagenomics offers a promising path toward faster and more precise diagnosis of Bartonella endocarditis. Its integration into clinical workflows, supported by continuous algorithm development, cost optimization, and standardized protocols, has the potential to improve patient outcomes in rare infectious diseases.

Source: Annals of Medicine and Surgery

DOI: 10.1097/ms9.0000000000003942

Authors: Awaisi Muhammad Abdur Rehman, Muhammad Hashim Rizwan, Maliha Khalid, Muhammad Talha, Aminath Waafira

Institutions: King Edward Medical University, Jinnah Sindh Medical University, Maldives National University

Language: English

Topic: Bartonella species infections research

Subfield: Parasitology

Field: Immunology and Microbiology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,850